Back to Search Start Over

Resting-state functional connectivity predicts longitudinal pain symptom change in urologic chronic pelvic pain syndrome: a MAPP network study

Authors :
Alisa Stephens
Jennifer S. Labus
Scott Peltier
Connor Fling
A. Vania Apkarian
Sonja J. Fenske
Bogdan Petre
Katherine T. Martucci
Wensheng Guo
Daniel J. Clauw
Eric Ichesco
J. Richard Landis
Xiaoling Hou
Jason J. Kutch
Emeran A. Mayer
Chris Mullins
Sean Mackey
Richard E. Harris
Melissa A. Farmer
Source :
Pain. 158:1069-1082
Publication Year :
2017
Publisher :
Ovid Technologies (Wolters Kluwer Health), 2017.

Abstract

Chronic pain symptoms often change over time, even in individuals who have had symptoms for years. Studying biological factors that predict trends in symptom change in chronic pain may uncover novel pathophysiological mechanisms and potential therapeutic targets. In this study, we investigated whether brain functional connectivity measures obtained from resting-state functional magnetic resonance imaging at baseline can predict longitudinal symptom change (3, 6, and 12 months after scan) in urologic chronic pelvic pain syndrome. We studied 52 individuals with urologic chronic pelvic pain syndrome (34 women, 18 men) who had baseline neuroimaging followed by symptom tracking every 2 weeks for 1 year as part of the Multidisciplinary Approach to the Study of Chronic Pelvic Pain (MAPP) Research Network study. We found that brain functional connectivity can make a significant prediction of short-term (3 month) pain reduction with 73.1% accuracy (69.2% sensitivity and 75.0% precision). In addition, we found that the brain regions with greatest contribution to the classification were preferentially aligned with the left frontoparietal network. Resting-state functional magnetic resonance imaging measures seemed to be less informative about 6- or 12-month symptom change. Our study provides the first evidence that future trends in symptom change in patients in a state of chronic pain may be linked to functional connectivity within specific brain networks.

Details

ISSN :
18726623 and 03043959
Volume :
158
Database :
OpenAIRE
Journal :
Pain
Accession number :
edsair.doi.dedup.....4f03677fb9b44bb58aa9b22c79be7f78
Full Text :
https://doi.org/10.1097/j.pain.0000000000000886